{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xarray as xr, shutil, \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np, tqdm, pandas as pd, sys, os\n",
    "import scipy.odr as odr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perspective Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = [\n",
    "    'e6',\n",
    "    'e7',\n",
    "    'e8',\n",
    "    'e11',\n",
    "    'e12',\n",
    "        ]\n",
    "\n",
    "persp_data = dict([])\n",
    "\n",
    "for run in runs:\n",
    "    data = dict([])\n",
    "    test = pd.read_excel(r'./image_slope_measurements_data/gb_s3_'+run+'_persp.xlsx')\n",
    "    data['pix'] = test[test.Unit=='pixels'].Length.values[0]\n",
    "    xs = []\n",
    "    ls = []\n",
    "    for i in range(4):\n",
    "        xs.append(test[test.Measurement=='x_%s'%(i+1)].Length.values[0])\n",
    "        ls.append(test[test.Measurement=='%s'%(i+1)].Length.values[0])\n",
    "    data['x'] = np.array(xs)\n",
    "    data['l'] = np.array(ls)/ls[1]    # The 'truth' is the second one\n",
    "    persp_data[run]= data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e6 -4.814532614621507e-08 xshift =  254.25795240852548 1.0028070585048847\n",
      "e7 -5.023511686770511e-08 xshift =  54.26382367904539 1.0134178620044099\n",
      "e8 -5.378404037550728e-08 xshift =  72.47877383851002 1.0122493562138528\n",
      "e11 -5.075229794601864e-08 xshift =  22.281121167357377 1.0121600683405825\n",
      "e12 -4.711878396363473e-08 xshift =  9.040366929150702 1.0145101111314054\n",
      "-5.047255978821644e-08 39.51602140351587 1.0130843494225625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aa = []\n",
    "bb = []\n",
    "cc  = []\n",
    "for ii,run in enumerate(runs):\n",
    "    x = persp_data[run]['x']\n",
    "    l = persp_data[run]['l']\n",
    "    plt.plot(x,l,'o',label=run,color=(0,0,(ii+1)/len(runs),1))\n",
    "    ## Fitting\n",
    "    # Linear fit\n",
    "    def func(B, x): return B[0]*(x-B[1])**(2)+B[2]\n",
    "    pwrlaw = odr.Model(func)\n",
    "    mydata = odr.RealData(x,l)\n",
    "    myodr=odr.ODR(mydata,pwrlaw,beta0=[0.0,0.0,0.0])\n",
    "    myoutput=myodr.run()\n",
    "    a = myoutput.beta[0]\n",
    "    da =myoutput.sd_beta[0]\n",
    "    xshift =  myoutput.beta[1]\n",
    "    dxshift = myoutput.sd_beta[1]\n",
    "    c =  myoutput.beta[2]\n",
    "    dc = myoutput.sd_beta[2]\n",
    "\n",
    "    aa.append(a)\n",
    "    bb.append(xshift)\n",
    "    cc.append(c)\n",
    "    \n",
    "    print(run,a,'xshift = ',xshift,c)\n",
    "\n",
    "    xpl = np.linspace(np.min(x),np.max(x))\n",
    "    plt.plot(xpl,func([a,xshift,c],xpl),'--',color=(0,0,(ii+1)/len(runs),1))\n",
    "\n",
    "# Taking average of e7-e12\n",
    "am = np.mean(aa[1:])\n",
    "bm = np.mean(bb[1:])\n",
    "cm = np.mean(cc[1:])\n",
    "print(am,bm,cm)\n",
    "plt.plot(xpl,am*(xpl-50)**2+cm,'-r',lw=2)\n",
    "\n",
    "for ii,run in enumerate(runs):\n",
    "    x = persp_data[run]['x']\n",
    "    l = persp_data[run]['l']\n",
    "    l = l[1]*l/(am*(x-50)**2+cm)\n",
    "    plt.plot(x,l,'-o',label=run+' corrected',color=(0,(ii+1)/len(runs),0,1))\n",
    "plt.legend(loc=(1.01,0.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If only the perspective wouldn't change, ugh... But still it's an improvement.\n",
    "\n",
    "Now let's see if we can 'estimate' the shift of e1-e5, by just linear extrapolation (best we can do...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.64460505660694 -1388.5707091642307\n",
      "-39.451885586766366\n"
     ]
    }
   ],
   "source": [
    "xshift7 = bm\n",
    "xshift6 = 254.257952\n",
    "dx_plug7 = 540.\n",
    "dx_plug6 = 621.2\n",
    "\n",
    "m,b = np.polyfit([dx_plug7,dx_plug6],[xshift7,xshift6],1)\n",
    "print(m,b)\n",
    "\n",
    "dx_plug3 = 510.14\n",
    "\n",
    "xshift3 = m*dx_plug3+b\n",
    "print(xshift3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying to check if this might be reasonable by looking at the height of the top over distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####\n",
    "for ii,run in enumerate(['e3','e6','e7']):\n",
    "    imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_'+run+'_v2.xlsx')\n",
    "    len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)\n",
    "    bed_h_im = []\n",
    "    water_h_im = []\n",
    "    cal_h_im = []\n",
    "    dxs = [0.0]\n",
    "    for i in range(len_list):\n",
    "        bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "        water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "        cal_h_im.append(imdat[imdat.Measurement=='%s_c' % (i+1)].Length.values[0])\n",
    "        if i <(len_list-1):\n",
    "            dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "    # Include lens corrections\n",
    "    x = np.array(dxs)\n",
    "    bed_h_im = np.array(bed_h_im)\n",
    "    water_h_im = np.array(water_h_im)\n",
    "    l = np.array(cal_h_im)\n",
    "    plt.plot(x,l,'-o',color=(0,0,(ii+1)/len(runs),1))\n",
    "    if run in ['e6','e5']:\n",
    "        xshift = xshift6\n",
    "    elif run in ['e1','e2','e3','e4']:\n",
    "        xshift = xshift3\n",
    "    else:\n",
    "        xshift = xshift7\n",
    "    l = l/(func([am,xshift,cm],x))\n",
    "    plt.plot(x,l,'-o',color=(0,(ii+1)/len(runs),0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems reasonable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aa = []\n",
    "bb = []\n",
    "cc  = []\n",
    "for ii,run in enumerate(runs):\n",
    "    x = persp_data[run]['x']\n",
    "    l = persp_data[run]['l']\n",
    "    plt.plot(x,l,'o',label=run,color=(0,0,(ii+1)/len(runs),1))\n",
    "#     ## Fitting\n",
    "#     # Linear fit\n",
    "#     def func(B, x): return B[0]*(x-B[1])**(2)+B[2]\n",
    "#     pwrlaw = odr.Model(func)\n",
    "#     mydata = odr.RealData(x,l)\n",
    "#     myodr=odr.ODR(mydata,pwrlaw,beta0=[0.0,0.0,0.0])\n",
    "#     myoutput=myodr.run()\n",
    "#     a = myoutput.beta[0]\n",
    "#     da =myoutput.sd_beta[0]\n",
    "#     xshift =  myoutput.beta[1]\n",
    "#     dxshift = myoutput.sd_beta[1]\n",
    "#     c =  myoutput.beta[2]\n",
    "#     dc = myoutput.sd_beta[2]\n",
    "\n",
    "#     aa.append(a)\n",
    "#     bb.append(xshift)\n",
    "#     cc.append(c)\n",
    "    \n",
    "#     print(run,a,'xshift = ',xshift,c)\n",
    "\n",
    "    xpl = np.linspace(np.min(x),np.max(x))\n",
    "    if run in ['e6','e5']:\n",
    "        xshift = xshift6\n",
    "    elif run in ['e1','e2','e3','e4']:\n",
    "        xshift = xshift5\n",
    "    else:\n",
    "        xshift = xshift7\n",
    "    \n",
    "    plt.plot(xpl,func([am,xshift,cm],xpl),'--',color=(0,0,(ii+1)/len(runs),1))\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "for ii,run in enumerate(runs):\n",
    "    x = persp_data[run]['x']\n",
    "    l = persp_data[run]['l']\n",
    "    if run in ['e6','e5']:\n",
    "        xshift = xshift6\n",
    "    elif run in ['e1','e2','e3','e4']:\n",
    "        xshift = xshift5\n",
    "    else:\n",
    "        xshift = xshift7\n",
    "    l = l/(func([am,xshift,cm],x))\n",
    "    plt.plot(x,l,'-o',label=run+' corrected',color=(0,(ii+1)/len(runs),0,1))\n",
    "    \n",
    "plt.plot(xpl,func([am,xshift3,cm],xpl),'-r')\n",
    "plt.legend(loc=(1.01,0.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e6_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000 - 10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([8.6, 9.8, 11.0, 11.1 ,11.5, 11.5]), # cm (left to right)\n",
    "water_h = np.array([19.0, 20.8, 21.2, 21.5, 22.5, np.nan]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e6_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift6\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.035799999999999985\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04072396274490299\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 13.7541 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03619999999999999\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04356060112454696\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 20.3332 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e6']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e7_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000 - 10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([8.6, 9.7, 11.0, 11.5, 11.7, 12.4]), # cm (left to right)\n",
    "water_h = np.array([18.9, 20.4, 21.2, 21.3, 22.7, 23.5]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e7_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0384\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.040864199619391625\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 6.4172 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03939999999999996\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.038556264231544034\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 2.1415 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e7']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e8_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000-10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([9.0, 10.8, 12.2, 13.0, 13.7, 15.0]), # cm (left to right)\n",
    "water_h = np.array([18.2, 20.2, 21.2, 22.0, 23.5, 24.7]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e8_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04439999999999995\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04806090889765269\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 8.2453 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.046799999999999974\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0495962479227169\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 5.9749 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e8']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e9_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000-10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([9.3, 10.7, 12.5, 13.8, 14.5, 15.6]), # cm (left to right)\n",
    "water_h = np.array([18.1, 20.1, 21.5, 22.7, 24.1, 25.5]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e9_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05079999999999999\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04946978770635544\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 2.6185 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05179999999999996\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.050468146038849714\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 2.5711 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e9']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e10_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000-10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([9.5, 11.1, 13.3, 14.5, 15.6, 17.2]), # cm (left to right)\n",
    "water_h = np.array([18.0, 20.1, 22.1, 23.0, 24.8, 26.8]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e10_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05479999999999998\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05684607482800068\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 3.7337 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.055400000000000005\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05852964426555164\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 5.6492 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e10']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e11_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000-10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([10.6, 13.8, 18.4, 21.8, 25.6, np.nan]), # cm (left to right)\n",
    "water_h = np.array([17.8, 20.8, 25.1, 28.5, 33.0, np.nan]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e11_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10299999999999995\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],bed_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],bed_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1032276765777693\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 0.2210 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10539999999999997\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:5],water_h[0][1:5],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:5],water_h[0][1:5],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11017070663801984\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 4.5263 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e11']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e12_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken the old 'meta' file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "flume_slope = 35.4/1000-10/1000\n",
    "xs = np.array([10.0, 50.0, 100.0, 150.0, 200.0, 240.0]), # cm (left to right)\n",
    "bed_h = np.array([9.7, 12.0, 14.7, 16.3, 18.0, np.nan]), # cm (left to right)\n",
    "water_h = np.array([17.7, 20.4, 22.6, 24.9, 22.1, np.nan]), # cm (left to right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e12_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06839999999999993\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:4],bed_h[0][1:4],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:4],bed_h[0][1:4],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1 = dict([])\n",
    "data1['bed_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06985665909757993\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['bed_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 2.1296 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['bed_real']-data1['bed_im'])/data1['bed_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_bed'] = pce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07039999999999993\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs),np.max(xs),100)\n",
    "# plt.plot(xs[0][1:4],water_h[0][1:4],'.k',ms=10)\n",
    "m,b = np.polyfit(xs[0][1:4],water_h[0][1:4],1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Ruler Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_real'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07512434525497033\n"
     ]
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "# plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "# plt.plot(xp,m*xp+b)\n",
    "# plt.title('Image Measurements')\n",
    "print(m+flume_slope)\n",
    "data1['water_im'] = m+flume_slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent error = 6.7107 percent\n"
     ]
    }
   ],
   "source": [
    "# percent error:\n",
    "pce = abs((data1['water_real']-data1['water_im'])/data1['water_real'])*100\n",
    "\n",
    "print(\"Percent error = %.4f percent\" % pce)\n",
    "data1['pe_water'] = pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e12']=data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "types = [\n",
    "    'bed',\n",
    "#     'water',\n",
    "]\n",
    "mtype = ['o','x']\n",
    "runs = range(6,13)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "xdat = []\n",
    "ydat = []\n",
    "for ii,ttype in enumerate(types):\n",
    "    for run in range(6,13):\n",
    "        exp = 'e%s' % run\n",
    "        if run==6:\n",
    "            if exp!='e7' or exp!='e9':\n",
    "                xdat.append(data[exp][ttype+'_real'])\n",
    "                ydat.append(data[exp]['pe_'+ttype])\n",
    "            plt.scatter(data[exp][ttype+'_real'],data[exp]['pe_'+ttype],color='k',marker=mtype[ii],label=ttype)\n",
    "#             if ttype=='water':\n",
    "            plt.annotate(exp,(data[exp][ttype+'_real']+0.001,data[exp]['pe_'+ttype]))\n",
    "        else:\n",
    "            if exp!='e7' and exp!='e9':\n",
    "                xdat.append(data[exp][ttype+'_real'])\n",
    "                ydat.append(data[exp]['pe_'+ttype])\n",
    "            plt.scatter(data[exp][ttype+'_real'],data[exp]['pe_'+ttype],color='k',marker=mtype[ii])\n",
    "#             if ttype=='water':\n",
    "            plt.annotate(exp,(data[exp][ttype+'_real']+0.001,data[exp]['pe_'+ttype]))\n",
    "\n",
    "# ax=plt.gca()\n",
    "# ax.set_yscale('log')\n",
    "m_pce,b_pce = np.polyfit(xdat,np.log10(ydat),1)\n",
    "xpl = np.linspace(np.min(xdat),np.max(xdat),100)\n",
    "plt.plot(xpl,10**(m_pce*xpl+b_pce),'--r',label='exponential fit')\n",
    "plt.xlabel('Slope',fontsize=20)\n",
    "plt.ylabel('Percent Error',fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bed -0.0024630564291811554\n",
      "water -0.004556309041161161\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "types = [\n",
    "    'bed',\n",
    "    'water',\n",
    "]\n",
    "mtype = ['o','x']\n",
    "runs = range(6,13)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "\n",
    "for ii,ttype in enumerate(types):\n",
    "    xdat = []\n",
    "    ydat = []\n",
    "    for run in range(6,13):\n",
    "        exp = 'e%s' % run\n",
    "        if run==6:\n",
    "            if exp!='e7' or exp!='e9':\n",
    "                xdat.append(data[exp][ttype+'_real'])\n",
    "                ydat.append(data[exp][ttype+'_real']-data[exp][ttype+'_im'])\n",
    "            plt.scatter(data[exp][ttype+'_real'],data[exp][ttype+'_real']-data[exp][ttype+'_im'],color='k',marker=mtype[ii],label=ttype)\n",
    "#             if ttype=='water':\n",
    "#             plt.annotate(exp,(data[exp][ttype+'_real']+0.001,data[exp]['pe_'+ttype]))\n",
    "        else:\n",
    "            if exp!='e7' and exp!='e9':\n",
    "                xdat.append(data[exp][ttype+'_real'])\n",
    "                ydat.append(data[exp][ttype+'_real']-data[exp][ttype+'_im'])\n",
    "            plt.scatter(data[exp][ttype+'_real'],data[exp][ttype+'_real']-data[exp][ttype+'_im'],color='k',marker=mtype[ii])\n",
    "#             if ttype=='water':\n",
    "#             plt.annotate(exp,(data[exp][ttype+'_real']+0.001,data[exp]['pe_'+ttype]))\n",
    "    print(ttype,np.mean(ydat))\n",
    "\n",
    "# ax=plt.gca()\n",
    "# ax.set_yscale('log')\n",
    "# m_pce,b_pce = np.polyfit(xdat,np.log10(ydat),1)\n",
    "# xpl = np.linspace(np.min(xdat),np.max(xdat),100)\n",
    "# plt.plot(xpl,10**(m_pce*xpl+b_pce),'--r',label='exponential fit')\n",
    "\n",
    "plt.xlabel('Slope',fontsize=20)\n",
    "plt.ylabel('Real-Image',fontsize=20)\n",
    "plt.legend(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measurements of previously unmeasured slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gb_s3_e1 shift = 3\n",
      "gb_s3_e1 0.05636800692352497 bed\n",
      "gb_s3_e1 0.06390788239942352 water\n",
      "gb_s3_e2 shift = 3\n",
      "gb_s3_e2 0.035562089288927325 bed\n",
      "gb_s3_e2 0.038053117401571065 water\n",
      "gb_s3_e3 shift = 3\n",
      "gb_s3_e3 0.03522067166535292 bed\n",
      "gb_s3_e3 0.03541531171112086 water\n",
      "gb_s3_e4 shift = 3\n",
      "gb_s3_e4 0.03523441259014032 bed\n",
      "gb_s3_e4 0.03397583292805713 water\n",
      "gb_s3_e5 shift = 6\n",
      "gb_s3_e5 0.0449895506577936 bed\n",
      "gb_s3_e5 0.04635786371557446 water\n",
      "ng_s3_e2 shift = 7\n",
      "ng_s3_e2 0.03564279602131498 bed\n",
      "ng_s3_e2 0.03722846522299664 water\n",
      "ng_s3_e3 shift = 7\n",
      "ng_s3_e3 0.04622238696903873 bed\n",
      "ng_s3_e3 0.049618540409808884 water\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xshift7 = bm\n",
    "xshift6 = 254.257952\n",
    "dx_plug7 = 540.\n",
    "dx_plug6 = 621.2\n",
    "\n",
    "m,b = np.polyfit([dx_plug7,dx_plug6],[xshift7,xshift6],1)\n",
    "\n",
    "dx_plug3 = 510.14\n",
    "\n",
    "xshift3 = m*dx_plug3+b\n",
    "\n",
    "flume_slope = 35.4/1000 - 10/1000\n",
    "\n",
    "data_new = dict([])\n",
    "for run in range(1,8):\n",
    "    if run < 6:\n",
    "        exp = 'gb_s3_e%s' % run\n",
    "    elif run == 6:\n",
    "        exp = 'ng_s3_e2'\n",
    "    else:\n",
    "        exp = 'ng_s3_e3'\n",
    "    imdat = pd.read_excel(r'./image_slope_measurements_data/'+exp+'_v2.xlsx')\n",
    "    len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)\n",
    "    bed_h_im = []\n",
    "    water_h_im = []\n",
    "    dxs = [0.0]\n",
    "    for i in range(len_list):\n",
    "        bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "        water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "        if i <(len_list-1):\n",
    "            dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "    # Include lens corrections\n",
    "    if exp in ['gb_s3_e6','gb_s3_e5']:\n",
    "        print(exp,'shift = %s' % 6)\n",
    "        xshift = xshift6\n",
    "    elif exp in ['gb_s3_e1','gb_s3_e2','gb_s3_e3','gb_s3_e4']:\n",
    "        print(exp,'shift = %s' % 3)\n",
    "        xshift = xshift3\n",
    "    else:\n",
    "        print(exp,'shift = %s' % 7)\n",
    "        xshift = xshift7\n",
    "    xs_im = np.array(dxs)\n",
    "    bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "    water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))\n",
    "    \n",
    "    data1 = dict([])\n",
    "    \n",
    "    # Bed\n",
    "    xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "    plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "    m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "    plt.plot(xp,m*xp+b)\n",
    "    plt.title('Image Measurements')\n",
    "    print(exp,m+flume_slope,'bed')\n",
    "    Sb = m+flume_slope\n",
    "    data1['Sb'] = Sb-0.002\n",
    "    data1['dSb'] = Sb*2*10**(m_pce*Sb+b_pce)/100\n",
    "    \n",
    "    # Water\n",
    "    xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "    plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "    m,b = np.polyfit(xs_im,water_h_im,1)\n",
    "    plt.plot(xp,m*xp+b)\n",
    "    plt.title('Image Measurements')\n",
    "    print(exp,m+flume_slope,'water')\n",
    "    Sw = m+flume_slope\n",
    "    data1['Sw'] = Sw-0.002\n",
    "    data1['dSw'] = Sw*2*10**(m_pce*Sw+b_pce)/100\n",
    "    \n",
    "    if exp=='gb_s3_e1':\n",
    "        data1['d'] = np.mean(water_h_im[:-2]-bed_h_im[:-2])\n",
    "        data1['dd'] = np.std(water_h_im[:-2]-bed_h_im[:-2])/np.sqrt(len(water_h_im[:-2]-bed_h_im[:-2]))\n",
    "    else:\n",
    "        data1['d'] = np.mean(water_h_im-bed_h_im)\n",
    "        data1['dd'] = np.std(water_h_im-bed_h_im)/np.sqrt(len(water_h_im-bed_h_im))\n",
    "    \n",
    "    data_new[exp]=data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.DataFrame(data=data_new)\n",
    "\n",
    "# df.to_excel('./image_slope_meas.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e3_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e3_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift3\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03522067166535292\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03541531171112086\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.11585849788736"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e1_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e1_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift3\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05636800692352497\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06390788239942352\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.49590591076348"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im[:-2]-bed_h_im[:-2])\n",
    "\n",
    "np.mean(water_h_im[:-2]-bed_h_im[:-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e2_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e2_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift3\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.035562089288927325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.038053117401571065\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110.58431668213565"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e4_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e4_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift3\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03523441259014032\n"
     ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03397583292805713\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.27069295124312"
      ]
     },
     "execution_count": 572,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gb_s3_e5_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/gb_s3_e5_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift6\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0449895506577936\n"
     ]
    },
    {
     "data": {
      "image/png": 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7nugMXKSBqM/xuvJy58XPVnLfm/PYuaeM/znpcH540mBaNW9869DxpAIXaSDqa7xuzprtTJo6m89XbCVzQEfuOieVwd3a1uoxpHqqVeBm9jPge4ADs4ErgdbA34D+wDLgfHffUicpRaRKdT1eFykp47f/XMBfPlxG+1bN+M13R/Ht0b30d7rjqMopFDPrBXwAjHD3XWb2EvAGMALY7O73mtmNwGHufsOB9qUpFJG6tXcKpTbH69yd7MIi7nitkLXbdnPh2D7ccPowOrRuXkuppSqHOoXSFGhlZqXEzrzXADcB3wyufxqYCRywwEWkbqWkpDB+/PhaW/NeuXknt08r5O156xnWvS0PX5TOmH4da2XfcuiqLHB3X21m9wMrgF3AW+7+lpl1c/e1wW3WmlnXfd3fzK4FrgXo27dv7SUXkTqzp6ycJz5YykNvL6CJGTefMYwrjxtAM810J5QqC9zMDgMmAAOArcDfzeyS6h7A3R8FHoXYEkoNc4pIPfl06WYmTpnNwvXFnDaiG7edPZJeHVrFO5bsQ3WWUE4Blrr7BgAzewU4Figysx7B2XcPYH0d5hSROrY5sod73pjL32etoleHVjx+WQanjOgW71hyANUp8BXA0WbWmtgSyslALhABLgfuDd6/WlchRaTulJc7f5+1knvC8yjeXcb3TzycH588iNbNNWWc6KqzBp5jZi8DnwNlQB6xJZE2wEtmdjWxkv9uXQYVkdo3f90OJk6ZTe7yLRzV/zDuOucIhnbXTHeyqNZ/se5+G3Bbpc0lxM7GRSTJ7NxTxkNvL+SJ95fStmVTfnXekZw3ujdN9GLCSUWPkUQamX/OKeK2aYWs3rqL8zN6c2PWcDqGNNOdjFTgIo3E6q27uH1aITPmFDGkWxteuu4Yxg7QTHcyU4GLNHCl0XKe/HApD85YCMCNWcO4+njNdDcEKnCRBix32WYmTilgftEOThneldvOGkmfjq3jHUtqiQpcpAHaEtnDfW/O48XPVtKzfUsevXQMp43sHu9YUstU4CINiLvz8qxV3BOex7ZdpVx7wkB+cvJgQi30o94Q6V9VpIFYWLSDiVML+HTpZsb0O4y7zklleI928Y4ldUgFLpLkdu2J8vt3FvLoe0sItWjKPd8+gv/K6KOZ7kZABS6SxN6dt55bpxWwcvMuzhvTm5uyhtGpTYt4x5J6ogIXSUJrt+3iztfmEC5Yx6CubXjx2qM5emCneMeSeqYCF0kiZdFynvpoGQ/OWEBZufN/44ZyzTcG0rypZrobIxW4SJLIW7GFm6cUMHftdk4a2oU7J6RqpruRU4GLJLhtO0v5VfY8nv90Bd3atuSRS0YzbmR3vZiwqMBFEpW7MzV/NXe/PpfNkT1cddwAfnbqENpoplsC+k4QSUCLNxQzaUoBHy/ZxKg+HXjqyrGk9mof71iSYFTgIglkd2mUP767iEf+tYSWzZpw97mpXHhUX810yz6pwEUSxL8WbOCWqQWs2LyTc9J6MvHMEXRpq5lu2T8VuEicFW3fzZ3T5/D6l2sZ2CXE89/L5NhBneMdS5KAClwkTqLlzjMfL+M3by1gT7Scn586hOtOHEiLpinxjiZJQgUuEgdfrNzKxKmzKVi9nROGdOHOs0fSv3Mo3rEkyajARerRtl2l/Oat+fz1k+V0adOChy9K58wjemimW2pEBS5SD9ydaV+s4a7X57KpuITLj+nP9acNoW3LZvGOJklMBS5Sx5ZujHDL1AI+WLSRI3u35y+XH8URvTXTLYdOBS5SR3aXRnnkX4v547uLadG0CZMnjOSizH6kaKZbakmVBW5mQ4G/Vdg0ELgV6ABcA2wItt/s7m/UekKRJPT+wthM97JNOzl7VE8mnTmcru1axjuWNDBVFri7zwfSAMwsBVgNTAGuBB509/vrNKFIElm/fTeTX5/La1+sYUDnEM9encnxgzXTLXXjYJdQTgYWu/tyPWsu8h/Rcue5nOX8+s35lJSV89NTBvP9Ew+nZTPNdEvdOdgCvwB4ocLHPzSzy4Bc4Hp331L5DmZ2LXAtQN++fWuaUyRhzV61jYlTZ/Plqm0cP6gzk89JZYBmuqUemLtX74ZmzYE1wEh3LzKzbsBGwIHJQA93v+pA+8jIyPDc3NxDjCySGHbsLuU3by3gmY+X0THUglvPGsFZR2qmW2qfmc1y94zK2w/mDDwL+NzdiwD2vg92/hgw/ZBTiiQBd+f12Wu587U5bCgu4dKj+3H9aUNp30oz3VK/DqbAL6TC8omZ9XD3tcGH5wIFtRlMJBEt3xThllcLeW/BBlJ7teOxyzIY1adDvGNJI1WtAjez1sCpwHUVNv/KzNKILaEsq3SdSINSUhblz/9awsPvLqJ5ShNuP2sElx7TXzPdElfVKnB33wl0qrTt0jpJJJJgPlq0kUlTC1iyMcKZR/bg1vEj6KaZbkkA+k1MSRrRaJRwOExeXh7p6elkZWWRklJ3Y3obdpTwyzfmMiVvNX07tubpq8Zy4pAudXY8kYOlApekEI1GGTduHDk5OUQiEUKhEJmZmWRnZ9d6iZeXOy98toL7wvPYVRrlx98axA9OGqSZbkk4KnBJCuFwmJycHIqLiwEoLi4mJyeHcDjM+PHja+04hWu2MXFKAfkrt3LMwE5MPieVQV3b1Nr+RWqTClySQl5eHpFI5CvbIpEI+fn5tVLgxSVlPDhjAU9+uJSOoeb89r/SmJDWUzPdktBU4JIU0tPTCYVC/z4DBwiFQqSlpR3Sft2d7MJ13D5tDkU7dnPh2L7cMG4Y7VtrplsSnwpckkJWVhaZmZlfWwPPysqq8T5Xbt7JbdMKeWfeeob3aMcfLxnN6L6H1WJqkbqlApekkJKSQnZ2NuFwmPz8fNLS0mo8hbKnrJzH3l/C799ZSIoZk84czhXH9qdpSpM6SC5Sd6r9t1Bqg/4WisTbJ0s2MWlqAYvWF5OV2p1bzxpBj/at4h1L5IBq42+hiCStTcUl/PKNefzj81X0PqwVf7kig28N6xbvWCKHRAUuDVp5ufNS7kruCc8jUlLGD755OD/61mBaNddMtyQ/Fbg0WHPXbmfilNl8vmIrYwd05K5zUhnSrW28Y4nUGhW4NDiRkjIeenshT3ywlPatmnH/d0fxndG9NNMtDY4KXBqUtwrXcfu0QtZs280FR/XhhtOHcVioebxjidQJFbg0CKu27OT2aYX8c+56hnVvy+8uTCejf8d4xxKpUypwSWql0XKe+GApD/1zIQA3nzGMK48bQDPNdEsjoAKXpPXp0s1MmjqbBUXFnDaiG7edPZJeHTTTLY2HClySzubIHu4Nz+Wl3FX06tCKxy7L4NQRmumWxkcFLkmjvNx5+fNV3PPGXHbsLuO6Ewfyk5MH07q5vo2lcdJ3viSFBUU7mDhlNp8t20JGv8O4+9wjGNpdM93SuKnAJaHt3FPG795exOPvL6FNy6b86jtHct6Y3jTRiwmLqMAlcb09t4hbXy1k9dZdnJ/RmxuzhtNRM90i/6YCl4SzZusu7nitkOzCIoZ0a8NL1x3D2AGa6RapTAUuCaM0Ws5THy7jwX8uoNydG04fxtXHD6B5U810i+xLlQVuZkOBv1XYNBC4FXgm2N4fWAac7+5baj+iNAazlm9m4pQC5q3bwbeGdeWOs0fSp2PreMcSSWhVFri7zwfSAMwsBVgNTAFuBN5293vN7Mbg4xvqMKs0QFt37uG+N+fxwqcr6dG+JY9cMoZxI7vpD0+JVMPBLqGcDCx29+VmNgH4ZrD9aWAmKnCpJnfnlc9Xc/cbc9m2q5RrvjGAn54yhFALreqJVNfB/rRcALwQXO7m7msB3H2tmXXd1x3M7FrgWoC+ffvWNKc0IIvW72DS1AI+WbKZ0X07cPe5RzC8R7t4xxJJOtUucDNrDpwN3HQwB3D3R4FHIfaamAeVThqU3aVRHn5nEX9+bzGtmzflnm8fwX9l9NFMt0gNHcwZeBbwubsXBR8XmVmP4Oy7B7C+9uNJQ/Hu/PXc+moBKzfv4juje3PzGcPo1KZFvGOJJLWDKfAL+c/yCcA04HLg3uD9q7WYSxqIddt2c+f0Qt6YvY7Du4R44ZqjOebwTvGOJdIgVKvAzaw1cCpwXYXN9wIvmdnVwArgu7UfT5JVWbScpz9ezgNvzaes3Pm/cUO55hsDNdMtUouqVeDuvhPoVGnbJmJTKSJfkbdiCxOnFDBn7XZOHNKFyRNS6dtJM90itU0zW1Jrtu0q5dfZ83guZwVd27bgTxeP5vTU7prpFqkjKnA5ZO7Oq/lruOv1OWyO7OGKY/tz/WlDaaOZbpE6pZ8wOSSLNxRzy9QCPlq8iVF9OvDUlWNJ7dU+3rFEGgUVuNTI7tIof5y5mEdmLqZFsyZMPieVi8b2JUUz3SL1RgUuB+29BRu45dUClm/ayYS0nkw8czhd27aMdyyRRkcFLtVWtH03k6fPYfqXaxnYOcRz38vkuEGd4x1LpNFSgUuVouXOs58s5/7s+ZREy/n5qUO47sSBtGiaEu9oIo2aClwO6MtVW5k4pYDZq7fxjcGdmTwhlf6dQ/GOJSKowButaDRKOBwmLy+P9PR0srKySEn5zxn19t2l/CZ7Ps98spzObVrw+wvTGX9kD810iyQQFXgjFI1GGTduHDk5OUQiEUKhEJmZmWRnZ9OkSRNe+3Itk6fPYVNxCZcf05+fnzaEdi2bxTu2iFSiAm+EwuEwOTk5FBcXA1BcXExOTg5PvxLm3e1deH/hRo7o1Z4nLs/gyN4d4pxWRPZHBd4I5eXlEYlE/rMhpRkpo87i7llO6xZbuXPCSC7O7KeZbpEEpwJvhNLT0wmFQhQXF9Oy3yg6nvYDmnXsxajOziPXnkjXdprpFkkGKvBGKCsrizHHncT81qm0GHIc0W3r6LngH/z97se+8kSmiCQ2FXgjEy13nv90JZuO/h9CJaUc0XQtl2V15+wzVd4iyUYF3ogUrN7GxCmz+WLVNo4f1Jk7J4xkYJc28Y4lIjWkAm8Eduwu5YEZC3j6o2V0DLXgoQvSOHtUT810iyQ5FXgD5u68MXsdd04vZP2OEi7J7Mf/jhtK+1aa6RZpCFTgDdTyTRFufbWQfy3YwMie7fjzpRmk9dFMt0hDogJvYErKojz6ryU8/O4imqU04bazRnDp0f1omqIXExZpaFTgDcjHizcxcepslmyIcOYRPbhl/Ai6t9dMt0hDpQJvADYWl/DL1+fySt5q+nRsxZNXHsVJQ7vGO5aI1DEVeBIrL3de/Gwl94bnsqs0yo++NYj/OWkQLZtpnlukMVCBJ6k5a7Yzceps8lZsJXNAR+4+N5VBXdvGO5aI1KNqFbiZdQAeB1IBB64CxgHXABuCm93s7m/URUj5j+KSMn47YwFPfrSMDq2a8cD5ozg3vZdmukUaoeqegT8EvOnu55lZc6A1sQJ/0N3vr7N08m/uTnZhEXe8Vsi67bu5cGxffjFuKB1aN493NBGJkyoL3MzaAScAVwC4+x5gj8746s/KzTu5bVoh78xbz/Ae7fjDxaMZ3feweMcSkTirzhn4QGLLJE+a2ShgFvCT4LofmtllQC5wvbtvqXxnM7sWuBagb9++tRK6sdhTVs7jHyzhd28vpIkZk84czhXH9tdMt4gAYO5+4BuYZQCfAMe5e46ZPQRsBx4GNhJbE58M9HD3qw60r4yMDM/Nza2V4A1dzpJNTJpawML1xZw+sju3njWCnh1axTuWiMSBmc1y94zK26tzBr4KWOXuOcHHLwM3untRhZ0/BkyvlaSN3KbiEu4Jz+PlWavo1aEVT1yewcnDu8U7logkoCoL3N3XmdlKMxvq7vOBk4E5ZtbD3dcGNzsXKKjLoA1debnz91kruSc8j+LdZfz3Nw/nR98aROvmmvQUkX2rbjv8CHgumEBZAlwJ/M7M0ogtoSwDrquThI3AvHXbmTSlgNzlWziq/2Hcfe4RDOmmmW4RObBqFbi75wOV118urf04jcvOPWU89PZCnnh/KW1bNuXX5x3JeWN6a6ZbRKpFj8/jZMacIm6fVsjqrbu44Kg+3HD6MA4LaaZbRKpPBV7PVm/dxe3TCpkxp4ih3dry8vePIaN/x3jHEpEkpAKvJ6XRcp78cCkPzqeXHpwAAAtbSURBVFgIwE1Zw7jq+AE000y3iNSQCrwe5C7bzMQpBcwv2sEpw7txx4SR9NJMt4gcIhV4HdoS2cN9b87jxc9W0rN9Sx69dAynjewe71gi0kCowOuAu/PyrFXcE57Htl2lXHfCQH588mBCLfTlFpHao0apZQuLdjBxagGfLt3MmH6Hcdc5qQzv0S7esUSkAVKB15Jde6L8/p2FPPreEtq0bMp93zmC747pQ5MmmukWkbqhAq8F78wr4tZXC1m1ZRffGd2bm88YRqc2LeIdS0QaOBX4IVizdRd3vFZIdmERg7q24cVrj+bogZ3iHUtEGgkVeA2URst56sNlPPjPBZS783/jhnLNNwbSvKlmukWk/qjAD9Ks5VuYOGU289bt4KShXbhzQip9OraOdywRaYRU4NW0dece7ntzPi98uoLu7Vryp4tHc3pqd8rLy5k+fTp5eXmkp6eTlZVFSkpKvOOKSCOgAq+Cu/PK56v55Rtz2bqrlO8dP4CfnjqENi2aEo1GGTduHDk5OUQiEUKhEJmZmWRnZ6vERaTOqcAPYNH6HUyaWsAnSzaT1qcDz5ybysie7f99fTgcJicnh+LiYgCKi4vJyckhHA4zfvz4eMUWkUZCBb4Pu0ujPPzOIv783mJaNUvh7nNTufCovl+b6c7LyyMSiXxlWyQSIT8/XwUuInVOBV7JzPnrufXVQlZs3sm303tx0xnD6dJ23zPd6enphEKhf5+BA4RCIdLS0uorrog0YirwwLptu5k8fQ6vz17LwC4hnr8mk2MP73zA+2RlZZGZmfm1NfCsrKx6Si0ijVmjL/CyaDnPfLycB2YsoDRazv+eNoRrThhIi6ZVPwmZkpJCdnY24XCY/Px80tLSNIUiIvXG3L3eDpaRkeG5ubn1dryq5K/cysQpsylcs50Th3Thzgkj6dcpFO9YIiJfYWaz3L3y6xI3zjPwbbtK+XX2PJ7LWUHXti3448WjyUrtrhcTFpGk0qgK3N2Z9sUaJk+fy+ZICVceO4CfnTqYti2bxTuaiMhBazQFvmRDMbe8WsCHizYxqnd7nrryKFJ7ta/6jiIiCapaBW5mHYDHgVTAgauA+cDfgP7AMuB8d99SJykPwe7SKH+auZg/zVxMi2ZNmHxOKheN7UuK/k63iCS56p6BPwS86e7nmVlzoDVwM/C2u99rZjcCNwI31FHOGnl/4QZumVrAsk07OXtUTyaNH07Xti3jHUtEpFZUWeBm1g44AbgCwN33AHvMbALwzeBmTwMzSZACX799N5Nfn8trX6xhQOcQz16dyfGDDzzTLSKSbKpzBj4Q2AA8aWajgFnAT4Bu7r4WwN3XmlnXfd3ZzK4FrgXo27dvrYTen2i58+wny7k/ez4l0XJ+dsoQrjtxIC2baS5bRBqe6hR4U2A08CN3zzGzh4gtl1SLuz8KPAqxOfAapayG2au2cfOU2cxevY1vDO7M5Amp9O+smW4RabiqU+CrgFXunhN8/DKxAi8ysx7B2XcPYH1dhTyQ7btLeeCtBTzz8TI6tWnB7y9MZ/yRPTTTLSINXpUF7u7rzGylmQ119/nAycCc4O1y4N7g/at1mvTruZj+5VomT5/DhuISLju6H9ePG0o7zXSLSCNR3SmUHwHPBRMoS4ArgSbAS2Z2NbAC+G7dRPy6ZRsj3PJqAe8v3Ehqr3Y8fnkGR/buUF+HFxFJCNUqcHfPB772e/jEzsbrTUlZlEdmLuEPMxfRPKUJt501gsuO6a+ZbhFplJLmNzE/XLSRW6YWsGRjhPFH9uCW8SPo1k4z3SLSeCVFgd/zxlz+/N4S+nVqzdNXjeXEIV3iHUlEJO6SosDHDuhIi6ZN+MFJgzTTLSISSIoCP3l4N04e3i3eMUREEkqTeAcQEZGaUYGLiCQpFbiISJJSgYuIJCkVuIhIkkr4KZRoNEo4HCYvL4/09HSysrJISdEooYhIQhd4NBpl3Lhx5OTkEIlECIVCZGZmkp2drRIXkUYvoZdQwuEwOTk5FBcX4+4UFxeTk5NDOByOdzQRkbhL6ALPy8sjEol8ZVskEiE/Pz9OiUREEkdCF3h6ejqh0FdfVScUCpGWlhanRCIiiSOhCzwrK4vMzEzatGmDmdGmTRsyMzPJysqKdzQRkbhL6CcxU1JSyM7OJhwOk5+fT1pamqZQREQC5l5nrzP8NRkZGZ6bm1tvxxMRaQjMbJa7f+1FdRJ6CUVERPZPBS4ikqRU4CIiSUoFLiKSpFTgIiJJql6nUMxsA7C8hnfvDGysxTh1IRkyQnLkVMbakww5lfHA+rn7117NvV4L/FCYWe6+xmgSSTJkhOTIqYy1JxlyKmPNaAlFRCRJqcBFRJJUMhX4o/EOUA3JkBGSI6cy1p5kyKmMNZA0a+AiIvJVyXQGLiIiFajARUSSVFIUuJmdbmbzzWyRmd0Yxxx9zOxdM5trZoVm9pNge0czm2FmC4P3h1W4z01B7vlmNq4es6aYWZ6ZTU/EjGbWwcxeNrN5wdfzmETLGBz3Z8G/dYGZvWBmLeOd08z+YmbrzaygwraDzmRmY8xsdnDd78zM6jjjr4N/7y/NbIqZdYhnxv3lrHDd/5qZm1nneOfcL3dP6DcgBVgMDASaA18AI+KUpQcwOrjcFlgAjAB+BdwYbL8RuC+4PCLI2wIYEHweKfWU9efA88D04OOEygg8DXwvuNwc6JCAGXsBS4FWwccvAVfEOydwAjAaKKiw7aAzAZ8CxwAGhIGsOs54GtA0uHxfvDPuL2ewvQ+QTewXDzvHO+f+3pLhDHwssMjdl7j7HuBFYEI8grj7Wnf/PLi8A5hL7Id8ArFCInh/TnB5AvCiu5e4+1JgEbHPp06ZWW/gTODxCpsTJqOZtSP2g/MEgLvvcfetiZSxgqZAKzNrCrQG1sQ7p7u/B2yutPmgMplZD6Cdu3/ssQZ6psJ96iSju7/l7mXBh58AveOZcX85Aw8CvwAqTnnELef+JEOB9wJWVvh4VbAtrsysP5AO5ADd3H0txEoe6BrcLF7Zf0vsm6+8wrZEyjgQ2AA8GSzzPG5moQTLiLuvBu4HVgBrgW3u/lai5QwcbKZeweXK2+vLVcTOVCHBMprZ2cBqd/+i0lUJlROSo8D3tZYU19lHM2sD/AP4qbtvP9BN97GtTrOb2XhgvbvPqu5d9rGtrr++TYk9bP2Tu6cDEWIP+/cnLt8DwTryBGIPl3sCITO75EB32ce2eM/p7i9T3LKa2USgDHhu76b9ZInHz09rYCJw676u3k+euH0tk6HAVxFbj9qrN7GHsXFhZs2Ilfdz7v5KsLkoeBhF8H59sD0e2Y8DzjazZcSWm75lZs8mWMZVwCp3zwk+fplYoSdSRoBTgKXuvsHdS4FXgGMTMCc1yLSK/yxhVNxep8zscmA8cHGw3JBoGQ8n9h/2F8HPUG/gczPrnmA5geQo8M+AwWY2wMyaAxcA0+IRJHhm+Qlgrrs/UOGqacDlweXLgVcrbL/AzFqY2QBgMLEnO+qMu9/k7r3dvT+xr9U77n5JgmVcB6w0s6HBppOBOYmUMbACONrMWgf/9icTe94j0XLuPXa1MwXLLDvM7Ojgc7uswn3qhJmdDtwAnO3uOytlT4iM7j7b3bu6e//gZ2gVscGFdYmUs2LghH8DziA28bEYmBjHHMcTe2j0JZAfvJ0BdALeBhYG7ztWuM/EIPd86umZ6QrH/ib/mUJJqIxAGpAbfC2nAoclWsbguHcA84AC4K/EJhDimhN4gdiafCmxgrm6JpmAjODzWgw8TPCb2XWYcRGxNeS9PzuPxDPj/nJWun4ZwRRKPHPu702/Si8ikqSSYQlFRET2QQUuIpKkVOAiIklKBS4ikqRU4CIiSUoFLiKSpFTgIiJJ6v8BTY8Ljj1WnPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04635786371557446\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.27520929976063"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ng_s3_e2_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/ng_s3_e2_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04360047966775203\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:-2],bed_h_im[:-2],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03722846522299664\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101.10823126021133"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ng_s3_e3_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another way:\n",
    "imdat = pd.read_excel(r'./image_slope_measurements_data/ng_s3_e3_v2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "len_list = len(imdat[imdat.Measurement.str.contains('_b')].Length.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_h_im = []\n",
    "water_h_im = []\n",
    "dxs = [0.0]\n",
    "for i in range(len_list):\n",
    "    bed_h_im.append(imdat[imdat.Measurement=='%s_b' % (i+1)].Length.values[0])\n",
    "    water_h_im.append(imdat[imdat.Measurement=='%s_w' % (i+1)].Length.values[0])\n",
    "    if i <(len_list-1):\n",
    "        dxs.append(imdat[imdat.Measurement=='%s%s_dx' % (i+1,i+2)].Length.values[0])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "# Include lens corrections\n",
    "xshift = xshift7\n",
    "xs_im = np.array(dxs)\n",
    "bed_h_im = np.array(bed_h_im)/(func([am,xshift,cm],xs_im))\n",
    "water_h_im = np.array(water_h_im)/(func([am,xshift,cm],xs_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04622238696903873\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,bed_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im,bed_h_im,1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.049618540409808884\n"
     ]
    },
    {
     "data": {
      "image/png": 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yO5uPc/Lo16kFL9yQQGKM9dyfLiv6xpiQUlxaxsuLN/L3T9bRQIRxV/TntnNjrOe+mljRN8aEjCUb9jFhVhbr8wq5fGAnJl41gC6tm7kdq16xom+Mcd3ewiKe+GA172dsp1ubZrx2WyIj+1nPfU2wom+McU1ZmTL9q6085c3hcImfX404g3tH9KFZ4/qxYEkosqJvjHFF9o79jE/NImNrAcN7teWxq+Po3SF8e+5rixV9Y0ytKiwq5bkFa3n9i020ad6YZ687i2sSuh6zZKGpOVb0jTG1QlXxZu1i0pxs8g4WceOwaB4c1Y9WzRu5HS2sWNE3xtS4Lft8TJyVzWdr9zCwS0um3DyEhOg2bscKS1b0jTE1pqjUz9TPNvLip+tpFNGAiSkDuOXsHjS0nnvXWNE3xtSIL9fvZfysLDbu8XHloM5MSBlAp1ZN3Y4V9qzoG2Oq1Z6DRTz+wSpSM3fQo11z3rh9KBf17eB2LOOwom+MqRb+MuXtZVt5el4ORSVl3DeyN78c0ZumjaznPpRY0TfGnLaVufsZn7qSb3L3c27vdjw6Jo5e7SPdjmXKUZGVs14DUoA8VY1zxuIJLJHYlMAKWb9U1WUiEgOsBtY4hy9V1btrILcxJgQcOFLCs/PXMm3JZtp6mvD89fGMPquL9dyHsIpc6b8BvAhMCxp7Gpikql4RucL5/CJn2wZVja/OkMaY0KKqzPl2J4/OXcXewiJ+NrwHv7usL62aWc99qDtl0VfVRc4V/DHDQEvnditgR/XGMsaEqo17Cpk4K5vP1+9lUNdWvHprImd2a+12LFNBVZ3T/w2QJiJ/BRoA5wRt6ykiGcABYLyqLi7vDkRkLDAWIDo6uooxjDG15UiJn5cWbmDKwg00adiAyWMGclNSDyJsycI6papF/x7gflV9T0SuA14FLgF2AtGquk9EhgCpIjJQVQ8cfweqOhWYCoGF0auYwxhTCxat3cPEWVls3neI0Wd1YfyV/enQ0nru66KqFv1bgV87t/8LvAKgqkVAkXN7hYhsAGKB5aeZ0xjjgt0HjjB57io++HYnPaM8vHVnEuf1iXI7ljkNVS36O4ALgYXASGAdgIi0B/JV1S8ivYA+wMZqyGmMqUWl/jKmLdnCswvWUuwv4/5LYrnrwl7Wc18PVKRlczqBzpwoEckFHgZ+ATwvIg2BIzhz88AFwGQRKQX8wN2qml8TwY0xNSNzWwHjZq4ke8cBLohtz+TRA4mJ8rgdy1STinTv3HCCTUPK2fc94L3TDWWMqX37D5Xwl/k5/Dt9Kx1aNOEfNw7mikGdrOe+nrFX5BoT5lSV1MztPP7BavJ9xdx2Tgy/vTSWFk2t574+sqJvTBhbn1fIhNQslmzcR3z31rxx+zDiurZyO5apQVb0jQlDh4v9vPjpOqYu2kizRhE8fk0cNwyNpoH13Nd7VvSNCTOf5uQxcXYW2/IP86OErvzpyv5ERTZxO5apJVb0jQkTO/cfZtLsVczL3sUZ7T28/YskzjnDeu7DjRV9Y+q5Un8Zb3y5mecWrKW0TPn9qL784vxeNG5oSxaGIyv6xtRjX2/9jnEzs1i98wAj+3Vg0uiBdG/b3O1YxkVW9I2phwoOFfPUvBymL9tG51ZNmXLzEEYN7Gg998aKvjH1iary7opc/uzNYf/hEn5+Xk/uvzQWTxP7VTcB9j/BmHpi7e6DjJ+ZxbLN+QyObs3j1wyif+eWpz7QhBUr+sbUcYeKS3nh4/W8sngjkU0b8tSPB/GTId1/0HPv9/vxer1kZGSQkJBAcnIyERH2Bmrhxoq+MXXYglW7eWR2NtsLDvOTId14KLkf7crpuff7/YwaNYr09HR8Ph8ej4ekpCTS0tKs8IcZK/rG1EHbCw7zyOxsFqzaTWzHSGbcdTbDerY94f5er5f09HQKCwsBKCwsJD09Ha/XS0pKSm3FNiHAir4xdUiJv4xXP9/E8x+tA+Ch5H7ceV5PGkWcvOc+IyMDn893zJjP5yMzM9OKfpixom9MHbFsUz7jU1eydnchl/TvyCOjB9CtTcV67hMSEvB4PN9f6QN4PB7i4+NrKq4JUVb0jQlx+b5i/vzhav67IpeurZvx8i2JXDqgY6XuIzk5maSkpB/M6ScnJ9dQahOqKrJy1mtACpCnqnHOWDwwBWgKlAK/VNVlzrY/AncSWDnrPlVNq6HsxtRrZWXKjOXbeHJeDoVHSrn7wjO47+LeNG9c+Wu1iIgI0tLS8Hq9ZGZmEh8fb907YUpU9eQ7iFwAFALTgor+fOA5VfWKyBXAg6p6kYgMAKYDw4AuwEdArKr6T3aOxMREXb7c1k435qicXQcYNzOLFVu+Y1hMWx67Jo7Yji3cjmVCjIisUNXEyhxTkeUSF4lIzPHDwNFXfbQisFA6wBjgHVUtAjaJyHoCfwCWVCaUMeHKV1TK8x+v49XPN9GqWSP+cu2ZXDukm719gqk2VZ3T/w2QJiJ/BRoA5zjjXYGlQfvlOmM/ICJjcRZUj46OrmIMY+oHVSUtezeT52SzY/8Rrh/anT9c3o82nsZuRzP1TFWL/j3A/ar6nohcB7wKXAKUdzlS7vyRqk4FpkJgeqeKOYyp87blH+KR2dl8nJNHv04t+PuNCQzpceKee2NOR1WL/q3Ar53b/wVecW7nAt2D9uvG/6Z+jDFBikvLeHnxRv7+yToaiDDuiv7cfm4MDU/Rc2/M6ahq0d8BXAgsBEYC65zx2cDbIvIsgSdy+wDLTjOjMfXOkg37mDAri/V5hSTHdWLiVQPo3KqZ27FMGKhIy+Z04CIgSkRygYeBXwDPi0hD4AjO3LyqZovIDGAVgVbOX52qc8eYcLK3sIgnPlzN+19vp3vbZrx+21BG9OvgdiwTRirSvXPDCTYNOcH+jwOPn04oY+qbsjJl+ldbeXreGg4Vl3LviN78akRvmjW2PnlTu+wVucbUsKzt+xmfmkXmtgKG92rLY1fH0buD9dwbd1jRN6aGHDxSwrML1vKvLzfT1tOY5356FlfHd7Wee+MqK/rGVDNV5cOVu5g8N5u8g0XcOCyaB0f1o1XzRm5HM8aKvjHVacs+HxNmZbNo7R4GdmnJlJuHkBDdxu1YxnzPir4x1aCo1M8/P9vIi5+up3FEAyamDOCWs3tYz70JOVb0jTlNX6zfy4TULDbu9TE4Son1fUOHghIEe3sRE3qs6BtTRXkHj/DY3NXM/mYH0W2b03nt+yz4xwxSfT7+YWvQmhBljz2NqSR/mTJtyWYufuYz5mXt4tcX9+H+fj6yFsygsLAQVT1mDVpjQokVfWMqYWXufq556QsmzsrmrG6tmfeb87n/0liyvjnxGrTGhBKb3jGmAg4cKeGZtDW8uXQLbT1NeOGGBK46s/P3Pfe2Bq2pK6zoG3MSqsqcb3fy6NxV7C0s4pbhPfjdqL60bHpsz72tQWvqCiv6xpzApr0+JqRm8fn6vQzq2opXb03kzG6ty93X1qA1dcUp18itDbZGrgklR0r8vLRwA1MWbqBJwwb8/vK+3JTUg4gG9vYJJrTUyBq5xoSTRWv3MHFWFpv3HWJMfBfGXdmfDi2auh3LmGpjRd8YYPeBI0yeu4oPvt1JrygP//55Euf2jnI7ljHVzoq+CWul/jKmLdnCswvWUuwv47eXxnLXhb1o0tDm4k39VJGVs14DUoA8VY1zxv4D9HV2aQ0UqGq8iMQAq4E1zralqnp3dYc2pjpkbitg3MyVZO84wIWx7Zk8ZiA92nncjmVMjarIlf4bwIvAtKMDqvrTo7dF5Blgf9D+G1TVmpNNyNp/qISn03J4e9lWOrRowj9uHMwVgzrZ+9ybsFCR5RIXOVfwPyCB35LrCCyObkxIU1VmZmzniQ9Xk+8r5vZzevLby2KJbGKznCZ8nO7/9vOB3aq6Lmisp4hkAAeA8aq6uLwDRWQszoLq0dH2boSmZq3PK2R86kqWbswnvntr/nXHMAZ2aeV2LGNq3ekW/RuA6UGf7wSiVXWfiAwBUkVkoKoeOP5AVZ0KTIVAn/5p5jCmXIeL/fz9k3W8vHgjzRpF8MQ1g7h+aHcaWM+9CVNVLvoi0hD4ETDk6JiqFgFFzu0VIrIBiAXslVem1n2Ss5uJs7LJ/e4wPxrclT9d0Z+oyCZuxzLGVadzpX8JkKOquUcHRKQ9kK+qfhHpBfQBNp5mRmMqZUfBYSbNySYteze9O0TyztjhDO/Vzu1YxoSEirRsTgcuAqJEJBd4WFVfBa7n2KkdgAuAySJSCviBu1U1v3ojG1O+En8Zb3yxmec+WkuZKr8f1ZdfnN+Lxg3tHcSNOaoi3Ts3nGD8tnLG3gPeO/1YxlTOii35jJuZRc6ug4zs14FJowfSvW1zt2MZE3KsV83Uad/5inlqXg7vfLWNzq2aMuXmIYwa2NF67o05ASv6pk5SVf67IpcnvTnsP1zC2At68euL++CxnntjTsp+Q0xI8vv9eL1eMjIySEhIOOa96dfuPsj4mVks25zPkB5teOzqOPp3bulyYmPqBiv6JuT4/X5GjRr1g1WoZs75gBcXbuTVxZuIbNqQp348iJ8MsZ57YyrDir4JOV6vl/T09O/Xmy0sLOTrvFIueHIB3xUJ1yV246Hk/rT1NHY5qTF1jxV9E3IyMjLw+XwARLRsT9tL7qJ5n+FQcpj/3j2SoTFtXU5oTN1lRd+EnISEBDwtWtKg/yW0OifQMez78i2ef+AmK/jGnCYr+ibkRPVPovNtL1DcrB2H1i2l6Ms3GTqwNylXJrsdzZg6z4q+CRn7Cov4szeHd1fk0rVzV1I6H8Yf2YT42188pnvHGFN1VvSN68rKlBnLt/HkvBwKj5Ry94VncN/FvWneuCGBRduMMdXFir5x1eqdBxg3cyVfby1gWM+2PHZ1HLEdW7gdy5h6y4q+cYWvqJTnFqzl9S8306pZI/5y7ZlcO6SbvX2CMTXMir6pVarKvKxdTJqzil0HjnDDsGj+cHlfWje3nntjaoMVfVNrtuUfYuKsLD5ds4f+nVvyj5sGM6RHG7djGRNWrOibGldcWsbLizfywsfraNhAGH9lf247J4aGEfY+98bUNiv6pkYt2bCP8akr2bDHR3JcJyZeNYDOrZq5HcuYsFWRlbNeI9A3l6eqcc7Yf4C+zi6tgQJVjXe2/RG4k8DKWfepalpNBDehbc/BIp74cDUzM7bTvW0zXr99KCP6dnA7ljFhryJX+m8ALwLTjg6o6k+P3haRZ4D9zu0BBJZRHAh0AT4SkVhV9VdjZhPCysqU6V9t5SlvDodL/Nw7ojf3juxN00b2wipjQkFFlktcJCIx5W2TQH/ddcBIZ2gM8I6qFgGbRGQ9MAxYUi1pTUjL2r6f8alZZG4rYHivtjx29SB6d4h0O5YxJsjpzumfD+xW1XXO512BpUHbc52xHxCRscBYgOjo6NOMYdx08EgJzy5Yy7++3ExbT2P+9tN4xsR3sZ57Y0LQ6Rb9G4DpQZ+X91uu5R2oqlOBqQCJiYnl7mNCm6rywcqdPDp3FXkHi7hxWDQPjupHq+aN3I5mjDmBKhd9EWkI/AgYEjScC3QP+rwbsKOq5zCha/NeHxNmZbF43V4GdmnJP3+WSHz31m7HMsacwulc6V8C5KhqbtDYbOBtEXmWwBO5fYBlp3EOE2KKSv1MWbiRfyxcT+OIBjx81QB+NryH9dwbU0dUpGVzOnARECUiucDDqvoqgS6d4KkdVDVbRGYAq4BS4FfWuVN/fLF+LxNSs9i410fKmZ2ZkDKAji2buh3LGFMJour+dHpiYqIuX77c7RjmBPIOHuGxuauZ/c0OerRrzqNj4rggtr3bsYwJeyKyQlUTK3OMvSLXnJC/THlr6Rb+mraGotIyfn1xH+656AzruTemDrOiHyb8fj9er5eMjAwSEhJOuRLVt7kFjJuZxcrt+zmvdxSTxwykV3vruTemrrOiHwb8fj+jRo0iPT0dn8+Hx+MhKSmJtLS0HxT+/YdLeGb+Gt5cuoWoyCa8cEMCV53Z2XrujaknrOiHAa/XS3p6OoWFhQAUFhaSnp6O1+slJSWwHKGqMvubHTw6dzX5viJuPTuG314WS8um1nNvTH1iRT8MZGRk4PP5jhnz+XxkZmaSkpLChj2FTHMFqGcAAA7rSURBVJyVxRfr93FWt1a8fttQBnVr5VJaY0xNsqIfBhISEvB4PN9f6QN4PB4GDIrn2flrmPLZRpo0asCjYwZyY1IPIhrYVI4x9ZUV/TCQnJxMUlLSMXP6cZdex9/WeNiav56r47vwpyv706GF9dwbU99Z0Q8DERERpKWl4fV6+eLrLNY0j+PrvUKvBsK/f57Eub2j3I5ojKklVvTDhCLsbj2QOf5GFBeU8btLezP2wl40aWg998aEEyv6YSBj63eMm5nFqp0HuDC2PZPHDKRHO4/bsYwxLrCiX4/tP1TCU2k5TF+2lQ4tmvDSTYNJjutkPffGhDEr+vWQqjIzYzuPf7Ca7w4Vc8e5Pbn/0lgim9iP25hwZ1Wgnlmfd5DxqVks3ZhPQnRrpt05jIFdrOfeGBNgRb+eOFzs5++frOPlxRtp3rghT1wziOuHdqeB9dwbY4JY0a8HPsnZzcRZ2eR+d5gfDe7Kn67oT1RkE7djGWNCkBX9OmxHwWEmzckmLXs3vTtE8s7Y4Qzv1c7tWMaYEFaRlbNeA1KAPFWNCxr/P+BeAitkfaCqD4pIDLAaWOPstlRV767u0OGuxF/G619s4m8fraNMlQcv78vPz+tF44a2ZKEx5uQqcqX/BvAiMO3ogIiMAMYAZ6pqkYh0CNp/g6rGV2tK873lm/MZn5pFzq6DXNyvA4+MHkj3ts3djmWMqSNOWfRVdZFzBR/sHuBJVS1y9smr/mgm2He+Yp6al8M7X22jS6um/PNnQ7hsQEfruTfGVEpV5/RjgfNF5HHgCPCAqn7lbOspIhnAAWC8qi4u7w5EZCwwFiA6OrqKMeq/sjLl3a9z+fOHqzlwpJS7LujFfRf3wWM998aYKqhq5WgItAGGA0OBGSLSC9gJRKvqPhEZAqSKyEBVPXD8HajqVGAqBBZGr2KOem3NroOMT13JV5u/I7FHGx67Jo5+nVq6HcsYU4dVtejnAu+rqgLLRKQMiFLVPcDRKZ8VIrKBwKOC5dWSNkwcKi7l+Y/X8eriTbRo2pCnf3wm1w7pZj33xpjTVtWinwqMBBaKSCzQGNgrIu2BfFX1O1f+fYCN1RM1PCxYtZtHZmezveAwP03szkPJ/Wjjaex2LGNMPVGRls3pwEVAlIjkAg8DrwGviUgWUAzcqqoqIhcAk0WkFPADd6tqfo2lr0dyvzvEI7Oz+Wh1Hn07tuDdu88mMaat27GMMfVMRbp3bjjBppvL2fc94L3TDRVOikvLePXzTbzw8ToA/pjcjzvO60mjCOu5N8ZUP2sBcVH6xn2MT81iXV4hlw3oyMOjB9K1dTO3Yxlj6jEr+i7YV1jEEx/m8N7XuXRt3YxXb03k4v4d3Y5ljAkDVvRrUVmZ8p/l23jSm4OvqJR7LjqD+0b2oVljW7LQGFM7rOjXklU7DjA+dSVfby1gWM+2PH51HH06tnA7ljEmzFjRr2GFRaU8t2Atb3y5mVbNGvHMT87iR4O72tsnGGNcYUW/hqgq87J2MWnOKnYdOMINw6L5w+V9ad3ceu6NMe6xol8Dtu47xMTZWSxcs4f+nVvy0s2DGRzdxu1YxhhjRb86FZX6eXnRRv7+yXoaNhAmpAzg1rN70NB67o0xIcKKfjX5csNexqdmsXGPj+S4Tky8agCdW1nPvTEmtNTpou/3+/F6vWRkZJCQkEBycjIREbXb/rjnYBFPfLiamRnb6d62Ga/fPpQRfTuc+kBjjHFBnS36fr+fUaNGkZ6ejs/nw+PxkJSURFpaWq0U/rIy5e1lW3l6Xg6HS/zcO6I3947sTdNG1nNvjAlddbboe71e0tPTKSwsBKCwsJD09HS8Xi8pKSk1eu6s7fsZl5rFN9sKOLtXOx69Oo7eHSJr9JzGGFMd6mzRz8jIwOfzHTPm8/nIzMyssaJ/8EgJz8xfy7Qlm2nraczffhrPmPgu1nNvjKkz6mzRT0hIwOPxfH+lD+DxeIiPr/412VWVD1buZPKcVewpLOLmpB48MKovrZo1qvZzGWNMTaqzRT85OZmkpKQfzOknJydX63k27/UxYVYWi9ftJa5rS6bekkh899bVeg5jjKktFVlE5TUgBchT1big8f8D7gVKgQ9U9UFn/I/AnQQWUblPVdNqInhERARpaWl4vV4yMzOJj4+v1u6dIyV+pny2gZcWbqBxRAMeuWoAPzs7hghbstAYU4dJYJnbk+wQWA2rEJh2tOiLyAhgHHClqhaJSAdVzRORAcB0YBjQBfgIiFVV/8nOkZiYqMuXh84yup+v28uEWVls2usj5czOTEgZQMeWTd2OZYwxxxCRFaqaWJljKrJy1iIRiTlu+B7gSVU9ugh6njM+BnjHGd8kIusJ/AFYUplQbsk7cIRHP1jNnG92ENOuOW/eOYzz+7R3O5YxxlSbqs7pxwLni8jjwBHgAVX9CugKLA3aL9cZC2n+MuWtpVv4a9oaivxl/OaSPtx94RnWc2+MqXeqWvQbAm2A4cBQYIaI9ALKm/Aud/5IRMYCYwGio6OrGOP0fZtbwLiZWazcvp/z+0QxeUwcPaM8ruUxxpiaVNWinwu8r4EnBJaJSBkQ5Yx3D9qvG7CjvDtQ1anAVAjM6VcxR5XtP1zCM/PX8ObSLbSPbMLfb0gg5czO1nNvjKnXqlr0U4GRwEIRiQUaA3uB2cDbIvIsgSdy+wDLqiNodVFVZmXu4LEPVpPvK+LWs2P47WWxtGxqPffGmPqvIi2b04GLgCgRyQUeBl4DXhORLKAYuNW56s8WkRnAKgKtnL86VedObdqwp5CJs7L4Yv0+zurWijduH0pc11ZuxzLGmFpzypbN2lDTLZtHSvy89Ol6pny2kSaNGvDg5f24cVi09dwbY+q0GmnZrOsWrsnj4dnZbNl3iGsSuvKnK/rTvkUTt2MZY4wr6m3R37X/CJPnZvPhyl30au/h7Z8ncU7vKLdjGWOMq+pd0S/1l/GvJVt4dv4aSsuUBy6L5RcX9KJJQ+u5N8aYelX0M7Z+x7iZWazaeYCL+rZn8ug4ots1dzuWMcaEjHpR9AsOFfPUvDW889VWOrZoyv+7aTCXx3WqlZ77UFiy0RhjKqpOF31V5f2vt/PEh6spOFzCHef25P5LY4lsUjtflttLNhpjTGU1cDvA6fh8/V5+999viG7XnDn3nseElAG1VvDh2CUbVfWYJRuNMSYU1emif17vKF65JZH37j6HAV1a1vr5T7ZkozHGhKI6XfRFhEsGdKSBSy+yOrpkY7CaWrLRGGOqQ50u+m47umRjZGQkIkJkZGSNLNlojDHVpU4/keu2ml6y0RhjqltYvPeOMcbUR1V57x2b3jHGmDBiRd8YY8KIFX1jjAkjVvSNMSaMWNE3xpgwEhLdOyKyB9hyGncRRWCN3lBk2arGslVNKGeD0M5XF7P1UNX2lbmjkCj6p0tElle2bam2WLaqsWxVE8rZILTzhUs2m94xxpgwYkXfGGPCSH0p+lPdDnASlq1qLFvVhHI2CO18YZGtXszpG2OMqZj6cqVvjDGmAqzoG2NMGKnTRV9ELheRNSKyXkQecuH83UXkUxFZLSLZIvJrZ7ytiCwQkXXOv22Cjvmjk3eNiIyqhYwRIpIhInNDKZuItBaRd0Ukx/n+nR1C2e53fp5ZIjJdRJq6mU1EXhORPBHJChqrdB4RGSIiK51tL4jIaa8+dIJsf3F+rt+KyEwRaR0q2YK2PSAiKiJRoZRNRP7POX+2iDxdI9lUtU5+ABHABqAX0Bj4BhhQyxk6A4Od2y2AtcAA4GngIWf8IeAp5/YAJ2cToKeTP6KGM/4WeBuY63weEtmAfwE/d243BlqHQjagK7AJaOZ8PgO4zc1swAXAYCAraKzSeYBlwNmAAF4guYayXQY0dG4/FUrZnPHuQBqBF4RGhUo2YATwEdDE+bxDTWSry1f6w4D1qrpRVYuBd4AxtRlAVXeq6tfO7YPAagJFYwyBoobz79XO7THAO6papKqbgPUEvo4aISLdgCuBV4KGXc8mIi0J/Kd/FUBVi1W1IBSyORoCzUSkIdAc2OFmNlVdBOQfN1ypPCLSGWipqks0UC2mBR1TrdlUdb6qljqfLgW6hUo2x3PAg0BwF0soZLsHeFJVi5x98moiW10u+l2BbUGf5zpjrhCRGCABSAc6qupOCPxhADo4u9V25r8R+M9dFjQWCtl6AXuA152pp1dExBMK2VR1O/BXYCuwE9ivqvNDIdtxKpunq3O7tnPeQeAKNCSyichoYLuqfnPcJtezAbHA+SKSLiKficjQmshWl4t+eXNXrvSfikgk8B7wG1U9cLJdyxmrkcwikgLkqeqKih5SzlhNfT8bEnho+/9UNQHwEZiiOJHa/L61IXBl1RPoAnhE5OZQyFZBJ8pT6zlFZBxQCvz76NAJMtRKNhFpDowDJpa3+QQZavv3og0wHPg9MMOZo6/WbHW56OcSmJs7qhuBh+G1SkQaESj4/1bV953h3c5DL5x/jz5Mq83M5wKjRWQzgamvkSLyVohkywVyVTXd+fxdAn8EQiHbJcAmVd2jqiXA+8A5IZItWGXz5PK/aZYazykitwIpwE3O1EMoZDuDwB/zb5zfi27A1yLSKQSy4ZzrfQ1YRuARelR1Z6vLRf8roI+I9BSRxsD1wOzaDOD8FX4VWK2qzwZtmg3c6ty+FZgVNH69iDQRkZ5AHwJPxFQ7Vf2jqnZT1RgC35tPVPXmEMm2C9gmIn2doYuBVaGQjcC0znARae78fC8m8FxNKGQLVqk8zhTQQREZ7nxdtwQdU61E5HLgD8BoVT10XGbXsqnqSlXtoKoxzu9FLoFGjF1uZ3OkAiMBRCSWQIPD3mrPdrrPQrv5AVxBoGNmAzDOhfOfR+Dh1LdApvNxBdAO+BhY5/zbNuiYcU7eNVRDF0AFc17E/7p3QiIbEA8sd753qQQe1oZKtklADpAFvEmga8K1bMB0As8vlBAoVHdWJQ+Q6HxNG4AXcV6RXwPZ1hOYgz76OzElVLIdt30zTvdOKGQjUOTfcs71NTCyJrLZ2zAYY0wYqcvTO8YYYyrJir4xxoQRK/rGGBNGrOgbY0wYsaJvjDFhxIq+McaEESv6xhgTRv4/YMi4XLksSD0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xp = np.linspace(np.min(xs_im),np.max(xs_im),100)\n",
    "plt.plot(xs_im,water_h_im,'.k',ms=10)\n",
    "m,b = np.polyfit(xs_im[:],water_h_im[:],1)\n",
    "plt.plot(xp,m*xp+b)\n",
    "plt.title('Image Measurements')\n",
    "print(m+flume_slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.49078801902058"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(water_h_im-bed_h_im)\n",
    "\n",
    "np.mean(water_h_im-bed_h_im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
